Multidimensional spectral analysis of the ultrasonic radiofrequency signal for characterization of media

Abstract

The importance of the analysis of the radiofrequency signal is by now recognized in the field of tissue characterization via ultrasound. The RF signal contains a wealth of information and structural details that are usually lost in the B-Mode representation. The HyperSPACE (Hyper SPectral Analysis for Characterization in Echography) algorithm presented by the authors in previous papers for clinical applications is based on the radiofrequency ultrasonic signal. The present work describes the method in detail and evaluates its performance in a repeatable and standardized manner, by using two test objects: a commercial test object that simulates the human parenchyma, and a laboratory-made test object consisting of human blood at different dilution values. In particular, the sensitivity and specificity in discriminating different density levels were estimated. In addition, the robustness of the algorithm with respect to the signal-to-noise ratio was also evaluated.

Characterization of benign thyroid nodules with HyperSPACE (Hyper Spectral Analysis for Characterization in Echography) before and after percutaneous laser ablation: a pilot study

“…The method is based on the spectral analysis of the RF ultrasonic signal and consists of several steps fully described in previous published papers [27][28][29][30].…”

“…In the second step, the coefficients obtained from the decomposition are analyzed and processed in N dimensional spectral hyperspace in order to generate the HyperSPACE coefficients (hs) [30] that are subjected to a training phase. In the training phase, the hs coefficients, belonging to selected Regions of Interest (ROIs) on the B-mode image, are trained to identify sets of spectral parameters, called Configurations, able to characterize the investigated ROIs.…”

“…In the fourth step, called Classification, the algorithm detects the presence of the previously defined Configurations over the entire RF image. By processing hs coefficients [30], several clusters relating to each ROI are obtained (fig 1b) and the hyper volume that contains the combination of all clusters, relating to each ROI, is called Configuration (fig 1c).…”

“…The proposed investigation method for spectral analysis called HyperSPACE (Hyper SPectral Analysis for Characterization in Echography) [27][28][29][30] implements a sub-band decomposition of the local signal spectrum and operates in hyperspectral domain. Important results of this method were obtained from an experiment involving ten Italian hospital clinics conducted to differentiate the two most common breast pathologies [27][28][29][30].…”

“…Important results of this method were obtained from an experiment involving ten Italian hospital clinics conducted to differentiate the two most common breast pathologies [27][28][29][30].…”